Evasive acceleration quantifies driving risk as the minimum 2D constant relative acceleration needed to avoid collision and outperforms time-to-collision on warning timing, discrimination, and information retention across crash datasets.
arXiv preprint arXiv:2512.07507
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OVPD is a new virtual-physical fusion dataset with 20 testing clips totaling nearly 3 hours of multi-modal autonomous driving data for closed-loop evaluation.
citing papers explorer
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Driving risk emerges from the required two-dimensional joint evasive acceleration
Evasive acceleration quantifies driving risk as the minimum 2D constant relative acceleration needed to avoid collision and outperforms time-to-collision on warning timing, discrimination, and information retention across crash datasets.
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OVPD: A Virtual-Physical Fusion Testing Dataset of OnSite Auton-omous Driving Challenge
OVPD is a new virtual-physical fusion dataset with 20 testing clips totaling nearly 3 hours of multi-modal autonomous driving data for closed-loop evaluation.